CPU上で動作する自律走行向け学習ベース走行可能領域解析の限界を押し上げる
Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU
幾何特徴と見た目特徴をSVMで統合し、CPUのみでリアルタイムに走行可能領域を解析する手法を提案。深層学習手法と同等の精度89.2%をより少ない計算資源で達成した。
著者: Daniel Fusaro, Emilio Olivastri, Daniele Evangelista, Marco Imperoli, Emanuele Menegatti, Alberto Pretto
分類: cs.RO, cs.CV
原文アブストラクト
Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes and evaluates a real-time machine learning-based Traversability Analysis method that combines geometric features with appearance-based features in a hybrid approach based on a SVM classifier. In particular, we show that integrating a new set of geometric and visual features and focusing on important implementation details enables a noticeable boost in performance and reliability. The proposed approach has been compared with state-of-the-art Deep Learning approaches on a public dataset of outdoor driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying complexity, demonstrating its effectiveness and robustness. The method runs fully on CPU and reaches comparable results with respect to the other methods, operates faster, and requires fewer hardware resources.
